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Proceedings Paper

Adaptive VQ-based linear prediction for lossless compression of ultraspectral sounder data
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Paper Abstract

Contemporary and future ultraspectral sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, we propose a novel Adaptive Vector Quantization (VQ)-based Linear Prediction (AVQLP) method for ultraspectral data compression. The method is compared with several state-of-the-art methods such as CALIC, JPEG-LS and JPEG2000. The compression experiments show that our AVQLP method is the first to surpass the 4 to 1 lossless compression barrier for a selected set of AIRS ultraspectral sounder test data.

Paper Details

Date Published: 1 September 2006
PDF: 7 pages
Proc. SPIE 6300, Satellite Data Compression, Communications, and Archiving II, 630002 (1 September 2006); doi: 10.1117/12.683967
Show Author Affiliations
Bormin Huang, Univ. of Wisconsin, Madison (United States)
Alok Ahuja, Univ. of Wisconsin, Madison (United States)
Mitchell D. Goldberg, NOAA/NESDIS (United States)

Published in SPIE Proceedings Vol. 6300:
Satellite Data Compression, Communications, and Archiving II
Roger W. Heymann; Charles C. Wang; Timothy J. Schmit, Editor(s)

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